# Radio Resource Management

In downlink NOMA, power allocation has relied on fixed, equal, or centralized learned rules that struggle to serve weak-channel users and throughput at once. A Kampala International University team models each user as an independent learning agent, coordinates them with a shared reward over local observations, and builds imperfect-SIC residual interference into the learning environment. In a three-user simulation the scheme reached about 18 bps/Hz and a fairness index of 0.96 at 30 dB SNR, with outage below 10% at 10 dB.

In downlink NOMA, power allocation has relied on fixed, equal, or centralized learned rules that struggle to serve weak-channel users and throughput at once. A Kampala International University team models each user as an independent learning agent, coordinates them with a shared reward over local observations, and builds imperfect-SIC residual interference into the learning environment. In a three-user simulation the scheme reached about 18 bps/Hz and a fairness index of 0.96 at 30 dB SNR, with outage below 10% at 10 dB.

The work introduces GREQ, a two-phase hybrid model-learning framework: phase one uses a channel-statistics-based greedy algorithm to deterministically satisfy user QoS with minimal subchannels, and phase two exploits the OFDM property that inter-cell interference on a subchannel comes only from the same subchannel to decompose the large-scale MARL problem into independent per-subchannel QMIX subproblems, avoiding exponential action-space growth; in simulations with 50MHz bandwidth and different numerologies, GREQ improves throughput by up to 40% over optimization and learning baselines, achieves 100% constraint satisfaction, and activates only 20–80% of available subchannels.

The work introduces GREQ, a two-phase hybrid model-learning framework: phase one uses a channel-statistics-based greedy algorithm to deterministically satisfy user QoS with minimal subchannels, and phase two exploits the OFDM property that inter-cell interference on a subchannel comes only from the same subchannel to decompose the large-scale MARL problem into independent per-subchannel QMIX subproblems, avoiding exponential action-space growth; in simulations with 50MHz bandwidth and different numerologies, GREQ improves throughput by up to 40% over optimization and learning baselines, achieves 100% constraint satisfaction, and activates only 20–80% of available subchannels.

The authors propose RRM-GPT, an autoregressive framework that generates radio resource management decisions field by field as a language model generates text: an encoder maps heterogeneous network observations into common tokens and a decoder emits each decision conditioned on the network state and the fields already committed; in a 5G NR case study, a single model generates complete scheduling grants spanning user selection, timing, link adaptation, resource allocation, and control signaling, and transfers learned behavior to an unseen scenario without adaptation.

The authors propose RRM-GPT, an autoregressive framework that generates radio resource management decisions field by field as a language model generates text: an encoder maps heterogeneous network observations into common tokens and a decoder emits each decision conditioned on the network state and the fields already committed; in a 5G NR case study, a single model generates complete scheduling grants spanning user selection, timing, link adaptation, resource allocation, and control signaling, and transfers learned behavior to an unseen scenario without adaptation.